Direct marketing is an efficient method of selling that was introduced in the 1960's to take advantage of developments in automation and the study of advertising. Rather than advertise to the general population, marketing efforts are focused on those most likely to purchase, leading to much more cost-effective sales techniques. Based on scientific advertising principles, direct marketing is serviced by increasingly more automated warehousing, shipping, and collection techniques. The phrase was coined in 1961 by Lester Wunderman, commonly regarded as the father of the direct marketing field.
As stated by Wunderman, direct marketing is only effective when certain conditions are met. A prospect must have the ability, willingness, and readiness to buy before a direct sale can be consummated. These three conditions are illustrated using a traditional concept in sales and marketing, the sales funnel, illustrated in FIG. 1. Marketers attempt to develop an ideal customer profile and suitable means for communicating with the ideal audience, such as direct mail, telephone solicitations, advertising in trade publications, trade shows, or seminars. Those likely to be interested in a given product or service are termed suspects. Suspects that express an interest in the offer, i.e. a willingness to purchase, are known as prospects. Those prospects with the financial ability to purchase are called qualified prospects. Intenders are qualified prospects that are ready to purchase in the near future, and it is from the intenders that customers are finally generated. Of course, the sales funnel is a broad concept, and the order in which the three conditions (ability, willingness, readiness) are applied to narrow the pool of suspects may be rearranged. The basic principle of the sales funnel is that a more efficient movement down the funnel leads to a decreased marketing and sales cost per eventual purchase.
To date, most direct marketing work aims to ascertain the ability to pay, or the qualified prospects. It is relatively straightforward to use demographic or lifestyle data available from third party data enhancement services, such as household income, to determine a suspect or prospect's ability to pay. However, such data provides little information on willingness or readiness to purchase. Such limitations have been addressed by predictive modeling or surveys of buying intentions. Predictive modeling is a statistical technique that uses various attributes about a prospect to assess a likelihood of buying. These attributes include actual or predicted behavior, attitude as determined from surveys, and collected or overlaid demographic data. Those believed to be unlikely buyers are excluded from costly direct marketing programs, while the most likely buyers are the subjects of intense marketing efforts. Predictive modeling provides the advantages of low cost and scalability.
One disadvantage of predictive models is that they may miss key attributes and incorporate stale data. For example, predictive models are often used for predicting purchases of high-ticket items, such as automobiles. However, the intention to buy a new car is often known only after the fact, when new vehicle registration information is available. What is needed is a reliable leading indicator of buying intent. Surveying has been considered the standard for identifying intender leads-consumers with a stated intention of buying a particular class of product within a given time frame. Surveys include questions that assess buying intentions, for example, “Do you plan to buy a new car in the next six months?” The self-reported data is then used to identify intender leads. A significant problem with surveys is that they are inefficient, as measured by the response rate, and expensive, as measured by the cost per completed survey. Furthermore, a link between a consumer's stated future intention and actual action is somewhat tenuous.
Both predictive models and surveys also suffer from a delay between lead generation and distribution to a sales force for pursuit. Even if the leads are of high quality, by the time that they can be distributed using traditional methods, the leads are no longer “hot,” i.e. the purchase has already been completed.
With the rapid growth of the World Wide Web in recent years, a large amount of effort has been devoted to using the Web for advertising and for collecting marketing and demographic data. When a consumer accesses a web site, the web server often sends a cookie along with the requested page. The cookie is a text string that may be saved to the user's hard drive, and is then read by the web server when the user accesses the same site. The cookie can be used to record user preferences or browsing history. Many web sites have banner ads along the top of the page that are provided directly by an advertising company, which sends a cookie along with the banner ad. Advertising companies use cookies to determine what advertisements the user has already seen, so that the same advertisement is not displayed twice, and also to track the user's browsing history, a potentially useful indicator of the user's interests. Before sending a banner ad to a user, the cookie is read to determine the most effective advertisement to send.
Direct marketers have long collected databases of lifestyle data of people, such as household income, number of children in the family, and hobbies and interests. Examples include Polk's Lifestyle Selector, Acxiom's Infobase, or Metromail/Experian's INSOURCESM. Now that online behavior can be tracked, and often linked to a person's name, home address, and telephone number, databases containing both online and offline behavior data are being collected. Such information gives direct marketers a rich picture of a user, as well as a variety of means to target consumers, including mail, telephone, email, and banner ads. While the information that is currently gathered is useful for profiling consumers, it does not address the problem of generating intender leads. That is, the information does not indicate buying intent, but rather records past purchases and behavior. Similarly, the information used to determine which banner ad to send is based on past behavior and interests, and is not based on indicators of future purchases.